Development of a simplified model and nomogram in preoperative diagnosis of pediatric chronic cholangitis with pancreaticobiliary maljunction using clinical variables and MRI radiomics

Objective The aim of this study was to develop a model that combines clinically relevant features with radiomics signature based on magnetic-resonance imaging (MRI) for diagnosis of chronic cholangitis in pancreaticobiliary maljunction (PBM) children. Methods A total of 144 subjects from two institutions confirmed PBM were included in this study. Clinical characteristics and MRI features were evaluated to build a clinical model. Radiomics features were extracted from the region of interest manually delineated on T2-weighted imaging. A radiomics signature was developed by the selected radiomics features using the least absolute shrinkage and selection operator and then a radiomics score (Rad-score) was calculated. We constructed a combined model incorporating clinical factors and Rad-score by multivariate logistic regression analysis. The combined model was visualized as a radiomics nomogram to achieve model visualization and provide clinical utility. Receiver operating curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance. Results Jaundice, protein plug, and ascites were selected as key clinical variables. Eight radiomics features were combined to construct the radiomics signature. The combined model showed superior predictive performance compared with the clinical model alone (AUC in the training cohort: 0.891 vs. 0.767, the validation cohort: 0.858 vs. 0.731), and the difference was significant (p = 0.002, 0.028) in the both cohorts. DCA confirmed the clinical utility of the radiomics nomogram. Conclusion The proposed model that combines key clinical variables and radiomics signature is helpful in the diagnosis of chronic cholangitis in PBM children. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-023-01383-z.


Image acquisition
MR scans were conducted using 2 3.0-T MRI scanners (Discovery MR750w; GE Healthcare, Milwaukee, WI, USA; and Discovery MR750; GE Healthcare, Piscataway, NJ, USA), both having 32-channel body coils. Children who could not remain motionless during examinations were sedated with 0.5 mg/kg chloral hydrate.

Image segmentation
Regions of interest (ROIs) were manually segmented using 3D Slicer software (version 4.10.2; https://www.slicer.org) by the two radiologists, who were blinded to histopathological data. Lesion outlines were traced on all contiguous slices to generate three-dimensional ROIs.
To test feature stability, readers 1 and 2 extracted radiomics features from 50 randomly chosen patients, and reader 1 then repeated the same procedure 2 weeks later. We calculated inter-/intra-observer class correlation coefficients (ICCs) to evaluate the consistency and reproducibility of the generated features. Features with ICC > 0.75 in both intra-and inter-observer agreement analyses were then included in subsequent analyses. Reader 1 segmented the ROIs for the remaining images.

Feature extraction
To correct the different pixel spacing of the MR image volumes for patients in two institutions, all images were resampled to 1 × 1 × 1 mm³ voxels and their intensity Insights Imaging (2023) Yang Y, Zhang XX, Zhao L, Wang J, Guo WL range was normalized to 0 to 255. Then, feature extraction was conducted using the radiomics module in 3D Slicer 4.10.2 platform.
A total of 1223 radiomic features were ultimately extracted. The features can be categorized into the following 4 groups: (1) Shape-based features (14 features): these features describe threedimensional size and shape of the region of interest (ROI), such as volume, surface area, and diameter.
(2) First-order statistics features (18 features): these features are related to the gray tone distribution of the pixel intensity and mainly used to perform first-order statistics like means, standard deviation, kurtosis, skewness, uniformity, energy and entropy.
(4) LoG features (372 features) and Wavelet features (744 features):to enhance intricate patterns in the data invisible to the human eye, advanced filters, including